CrewAI is a framework for orchestrating role-playing, autonomous AI agents. By leveraging OpenInference, you can trace CrewAI operations and the underlying LLM calls, sending the data to Arize for observability.
This typically involves instrumenting CrewAI itself and the library it uses for LLM interactions (commonly LangChain or LiteLLM).
Launch Arize
To get started, sign up for a free Arize account and get your Space ID and API Key.
Install
Install CrewAI, its tools, the OpenInference instrumentors for CrewAI and the underlying LLM interaction library (e.g., LangChain), and Arize OTel packages.
# Install CrewAI and common tools
pip install crewai crewai-tools
# Install OpenInference instrumentor for CrewAI
pip install openinference-instrumentation-crewai
# Install OpenInference instrumentor for the underlying LLM handler
pip install openinference-instrumentation-langchain
# If CrewAI uses LiteLLM (check your CrewAI version/setup), you might need:
pip install openinference-instrumentation-litellm
# Install Arize OTel
pip install arize-otel
API Key Setup
Your CrewAI agents will likely require API keys for the LLMs and tools they use. Configure these as environment variables. The example below uses OpenAI and SerperDevTool.
Connect to Arize using arize.otel.register and apply the instrumentors for CrewAI and the LLM library.
# Import Arize OTel registration
from arize.otel import register
# Import OpenInference instrumentors, use either Langchain or LiteLLM
from openinference.instrumentation.crewai import CrewAIInstrumentor
from openinference.instrumentation.langchain import LangChainInstrumentor
from openinference.instrumentation.litellm import LiteLLMInstrumentor
# Setup OTel and Arize exporter
tracer_provider = register(
space_id="YOUR_SPACE_ID",
api_key="YOUR_API_KEY",
project_name="my-crewai-app"
)
# Instrument CrewAI
CrewAIInstrumentor().instrument(tracer_provider=tracer_provider)
# Instrument the underlying LLM call handler (e.g., LangChain)
LangChainInstrumentor().instrument(tracer_provider=tracer_provider)
# If using LiteLLM directly via a newer CrewAI version for model calls:
LiteLLMInstrumentor().instrument(tracer_provider=tracer_provider)
Run CrewAI Example
Define your agents and tasks, then kick off the crew.
import os
from crewai import Agent, Task, Crew, Process
from crewai_tools import SerperDevTool
# Ensure API keys are set in the environment as shown in "API Key Setup"
# os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
# os.environ["SERPER_API_KEY"] = "YOUR_SERPER_API_KEY"
search_tool = SerperDevTool()
# Define your agents with roles and goals
researcher = Agent(
role='Senior Research Analyst',
goal='Uncover cutting-edge developments in AI and data science',
backstory="""You work at a leading tech think tank.
Your expertise lies in identifying emerging trends.
You have a knack for dissecting complex data and presenting actionable insights.""",
verbose=True,
allow_delegation=False,
tools=[search_tool]
# llm=ChatOpenAI(model_name="gpt-3.5-turbo") # Optionally specify an LLM instance here
)
writer = Agent(
role='Tech Content Strategist',
goal='Craft compelling content on tech advancements',
backstory="""You are a renowned Content Strategist, known for your insightful and engaging articles.
You transform complex concepts into compelling narratives.""",
verbose=True,
allow_delegation=True
)
# Create tasks for your agents
task1 = Task(
description="""Conduct a comprehensive analysis of the latest advancements in AI in 2024.
Identify key trends, breakthrough technologies, and potential industry impacts.""",
expected_output="Full analysis report in bullet points",
agent=researcher
)
task2 = Task(
description="""Using the insights provided, develop an engaging blog
post that highlights the most significant AI advancements.
Your post should be informative yet accessible, catering to a tech-savvy audience.
Make it sound cool, avoid complex words so it doesn't sound like AI.""",
expected_output="Full blog post of at least 4 paragraphs",
agent=writer
)
# Instantiate your crew with a sequential process
crew = Crew(
agents=[researcher, writer],
tasks=[task1, task2],
verbose=False, # Set to True or 2 for more detailed logging
process=Process.sequential # Default is sequential, can be hierarchical
)
# Get your crew to work!
print("Kicking off the crew...")
result = crew.kickoff()
print("######################")
print("CrewAI run completed. Result:")
print(result)
print("######################")
print("Check Arize for traces.")
Observe
Traces from CrewAI operations (agent steps, task execution) and the underlying LLM calls (via LangChain/LiteLLM) will be streamed to your Arize account. This provides a comprehensive view of your multi-agent system.
